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Bioinformatics Advance Access originally published online on January 28, 2009
Bioinformatics 2009 25(5):621-627; doi:10.1093/bioinformatics/btp036
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques

Ryan Brenke 1,{dagger}, Dima Kozakov 2,{dagger}, Gwo-Yu Chuang 2,{dagger}, Dmitri Beglov 2,{dagger}, David Hall 2, Melissa R. Landon 1, Carla Mattos 3 and Sandor Vajda 2,*

1Program in Bioinformatics, 2Structural Bioinformatics Laboratory, Department of Biomedical Engineering, Boston University, Boston, MA, USA and 3Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, USA

*To whom correspondence should be addressed.


   Abstract

Motivation: The binding sites of proteins generally contain smaller regions that provide major contributions to the binding free energy and hence are the prime targets in drug design. Screening libraries of fragment-sized compounds by NMR or X-ray crystallography demonstrates that such ‘hot spot’ regions bind a large variety of small organic molecules, and that a relatively high ‘hit rate’ is predictive of target sites that are likely to bind drug-like ligands with high affinity. Our goal is to determine the ‘hot spots’ computationally rather than experimentally.

Results: We have developed the FTMAP algorithm that performs global search of the entire protein surface for regions that bind a number of small organic probe molecules. The search is based on the extremely efficient fast Fourier transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids, but can use only sums of correlation functions for scoring and hence is generally restricted to very simple energy expressions. The novelty of FTMAP is that we were able to incorporate and represent on grids a detailed energy expression, resulting in a very accurate identification of low-energy probe clusters. Overlapping clusters of different probes are defined as consensus sites (CSs). We show that the largest CS is generally located at the most important subsite of the protein binding site, and the nearby smaller CSs identify other important subsites. Mapping results are presented for elastase whose structure has been solved in aqueous solutions of eight organic solvents, and we show that FTMAP provides very similar information. The second application is to renin, a long-standing pharmaceutical target for the treatment of hypertension, and we show that the major CSs trace out the shape of the first approved renin inhibitor, aliskiren.

Availability: FTMAP is available as a server at http://ftmap.bu.edu/.

Contact: vajda{at}bu.edu

Supplementary information: Supplementary Material is available at Bioinformatics online.

{dagger}The authors wish it to be known that, in their opinion, the first four authors should be regraded as joint First Authors.

Associate Editor: Burkhard Rost


Received on October 9, 2008; revised on January 8, 2009; accepted on January 13, 2009

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